ArticleInfectious Disease Modelling2025
Modeling the impact of hospitalization-induced behavioral changes on the spread of COVID-19 in New York City.
Article in Infectious Disease Modelling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Resolving parameter uncertainty in SIR models through population-level serological surveillance: A synthetic study.Infectious Disease Modelling · 2026Article
- The paradox of neglecting changes in behavior: How standard epidemic models misestimate both transmissibility and final epidemic size.PLoS computational biology · 2026Article
- Estimating the effect of self-protection on transmission dynamics of SARS-CoV-2 in Germany in 2021: a modelling study.BMC public health · 2026Article
- Impact of high-order time-delayed information on epidemic propagation in multiplex networks.Infectious Disease Modelling · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic, caused by SARS-CoV-2, highlighted heterogeneities in human behavior and attitudes of individuals with respect to adherence or lack thereof to public health-mandated intervention and mitigation measures. This study is based on using mathematical modeling approaches, backed by data analytics and computation, to theoretically assess the impact of human behavioral changes on the trajectory, burden, and control of the COVID-19 pandemic during the first two waves in New York City. A novel behavior-epidemiology model, which considers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.